Babak's Daily Briefing

Monday, March 30, 2026

Sources: 16 | Total Articles: 64

• 1.South Korea puts 6G inside its national AI push

  South Korea has unveiled a three-year national roadmap aimed at becoming one of the world’s top three AI powers by 2028, with 6G commercialization positioned as part of that broader push.

• 2.b-com’s Open XG Hub targets one of telecom’s biggest gaps: turning experimentation into deployment

  In an interview with Peter Pietrzyk, Managing Director of 6GWorld, Patrick Savell, Head of Connectivity at b-com, said platforms such as Open XG Hub are designed to help bridge one of the industry’s most persistent challenges: moving promising ideas from research environments into deployable network systems. The bigger point is that, as telecom becomes more software-driven and AI-native, the bottleneck is increasingly less about invention and more about validation, integration, and operational readiness.

• 3.ODC’s $45M raise signals a bigger shift in AI-RAN, from network optimization to edge intelligence

  ORAN Development Company said it has closed a $45 million Series A backed by Booz Allen, Cisco Investments, Nokia, NVIDIA, AT&T, MTN and Telecom Italia to scale its U.S.-based Odyssey platform, which it positions as an AI-native RAN architecture combining communications, sensing and edge intelligence. The company said it plans to accelerate commercial deployment through 2026.

• 4.Lockheed Martin’s NetSense points to a bigger shift: 5G as drone-detection infrastructure

  Lockheed Martin’s latest NetSense prototype suggests that commercial 5G infrastructure could play a growing role in drone detection, adding momentum to the broader move toward sensing-enabled wireless networks.

• 5.AI Grid, Unpacked

  At GTC 2026, NVIDIA did not just promote another edge computing concept. It laid out a broader telecom thesis: operators, cable MSOs and distributed cloud providers could become the infrastructure layer that brings AI closer to the physical world, with AI-RAN and, eventually, 6G acting as part of that fabric.

• 1.Relational graph-driven differential denoising and diffusion attention fusion for multimodal conversation emotion recognition

  arXiv:2603.25752v1 Announce Type: new Abstract: In real-world scenarios, audio and video signals are often subject to environmental noise and limited acquisition conditions, resulting in extracted features containing excessive noise. Furthermore, there is an imbalance in data quality and information carrying capacity between different modalities. These two issues together lead to information distortion and weight bias during the fusion phase, impairing overall recognition performance. Most existing methods neglect the impact of noisy modalities and rely on implicit weighting to model modality importance, thereby failing to explicitly account for the predominant contribution of the textual modality in emotion understanding. To address these issues, we propose a relation-aware denoising and diffusion attention fusion model for MCER. Specif...

• 2.RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation

  arXiv:2603.25804v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed. To address this gap, we introduce \textbf{\texttt{RealChart2Code}}, a new large-scale benchmark with over 2,800 instances grounded in authentic datasets and featuring tasks with clear analytical intent. Crucially, it is the first benchmark to systematically evaluate chart generation from large-scale raw data and assess iterative code refinement in a multi-turn conversational setting. Our comprehensive evaluation of 14 leading VLMs on \texttt{RealChart2Code} reveals significant performance degradation compared to simpler benchmarks, highlighting their struggle...

• 3.Doctorina MedBench: End-to-End Evaluation of Agent-Based Medical AI

  arXiv:2603.25821v1 Announce Type: new Abstract: We present Doctorina MedBench, a comprehensive evaluation framework for agent-based medical AI based on the simulation of realistic physician-patient interactions. Unlike traditional medical benchmarks that rely on solving standardized test questions, the proposed approach models a multi-step clinical dialogue in which either a physician or an AI system must collect medical history, analyze attached materials (including laboratory reports, images, and medical documents), formulate differential diagnoses, and provide personalized recommendations. System performance is evaluated using the D.O.T.S. metric, which consists of four components: Diagnosis, Observations/Investigations, Treatment, and Step Count, enabling assessment of both clinical correctness and dialogue efficiency. The system a...

• 4.Gradient-Informed Training for Low-Resource Multilingual Speech Translation

  arXiv:2603.25836v1 Announce Type: new Abstract: In low-resource multilingual speech-to-text translation, uniform architectural sharing across languages frequently introduces representation conflicts that impede convergence. This work proposes a principled methodology to automatically determine layer-specific sharing patterns by mining training gradient information. Our approach employs three distinct analysis strategies: distance-based language clustering, self/cross-task divergence metrics for capacity allocation, and joint factorization coupled with canonical correlation analysis for subspace alignment. Extensive evaluation across four language pairs (using the SeamlessM4T-Medium architecture) demonstrates persistent improvements in translation quality metrics.

• 5.Methods for Knowledge Graph Construction from Text Collections: Development and Applications

  arXiv:2603.25862v1 Announce Type: new Abstract: Virtually every sector of society is experiencing a dramatic growth in the volume of unstructured textual data that is generated and published, from news and social media online interactions, through open access scholarly communications and observational data in the form of digital health records and online drug reviews. The volume and variety of data across all this range of domains has created both unprecedented opportunities and pressing challenges for extracting actionable knowledge for several application scenarios. However, the extraction of rich semantic knowledge demands the deployment of scalable and flexible automatic methods adaptable across text genres and schema specifications. Moreover, the full potential of these data can only be unlocked by coupling information extraction me...

• 1.Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control

  arXiv:2603.25771v1 Announce Type: new Abstract: Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-term outcomes under different constraints, has been used in infectious disease control to optimize the intervention strategies for controlling infectious disease spread and responding to outbreaks in recent years. The potential of RL for assisting public health sectors in preventing and controlling infectious diseases is gradually emerging and being explored by rapidly increasing publications relevant to COVID-19 and other infectious diseases. However, few surveys exclusively discuss this topic, that is, the development and application of RL approaches for optimizing strategies of non-pharmaceutical and pharmaceutical interventions of public hea...

• 2.Pure and Physics-Guided Deep Learning Solutions for Spatio-Temporal Groundwater Level Prediction at Arbitrary Locations

  arXiv:2603.25779v1 Announce Type: new Abstract: Groundwater represents a key element of the water cycle, yet it exhibits intricate and context-dependent relationships that make its modeling a challenging task. Theory-based models have been the cornerstone of scientific understanding. However, their computational demands, simplifying assumptions, and calibration requirements limit their use. In recent years, data-driven models have emerged as powerful alternatives. In particular, deep learning has proven to be a leading approach for its design flexibility and ability to learn complex relationships. We proposed an attention-based pure deep learning model, named STAINet, to predict weekly groundwater levels at an arbitrary and variable number of locations, leveraging both spatially sparse groundwater measurements and spatially dense weather ...

• 3.MAGNET: Autonomous Expert Model Generation via Decentralized Autoresearch and BitNet Training

  arXiv:2603.25813v1 Announce Type: new Abstract: We present MAGNET (Model Autonomously Growing Network), a decentralized system for autonomous generation, training, and serving of domain-expert language models across commodity hardware. MAGNET integrates four components: (1) autoresearch, an autonomous ML research pipeline that automates dataset generation, hyperparameter exploration, evaluation, and error-driven iteration; (2) BitNet b1.58 ternary training, enabling CPU-native inference via bitnet.cpp without GPU hardware; (3) DiLoCo-based distributed merging for communication-efficient aggregation of domain specialists; and (4) on-chain contribution tracking on the HOOTi EVM chain. We validate autoresearch through three case studies: video safety classification (balanced accuracy 0.9287 to 0.9851), cryptocurrency directional prediction (...

• 4.A Compression Perspective on Simplicity Bias

  arXiv:2603.25839v1 Announce Type: new Abstract: Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new light on this phenomenon through the lens of the Minimum Description Length principle, formalizing supervised learning as a problem of optimal two-part lossless compression. Our theory explains how simplicity bias governs feature selection in neural networks through a fundamental trade-off between model complexity (the cost of describing the hypothesis) and predictive power (the cost of describing the data). Our framework predicts that as the amount of available training data increases, learners transition through qualitatively different features -- from simple spurious shortcuts to complex features -- only when the reduction in data encoding cost ...

• 5.Incorporating contextual information into KGWAS for interpretable GWAS discovery

  arXiv:2603.25855v1 Announce Type: new Abstract: Genome-Wide Association Studies (GWAS) identify associations between genetic variants and disease; however, moving beyond associations to causal mechanisms is critical for therapeutic target prioritization. The recently proposed Knowledge Graph GWAS (KGWAS) framework addresses this challenge by linking genetic variants to downstream gene-gene interactions via a knowledge graph (KG), thereby improving detection power and providing mechanistic insights. However, the original KGWAS implementation relies on a large general-purpose KG, which can introduce spurious correlations. We hypothesize that cell-type specific KGs from disease-relevant cell types will better support disease mechanism discovery. Here, we show that the general-purpose KG in KGWAS can be substantially pruned with no loss of st...

• 1.ETA-VLA: Efficient Token Adaptation via Temporal Fusion and Intra-LLM Sparsification for Vision-Language-Action Models

  arXiv:2603.25766v1 Announce Type: new Abstract: The integration of Vision-Language-Action (VLA) models into autonomous driving systems offers a unified framework for interpreting complex scenes and executing control commands. However, the necessity to incorporate historical multi-view frames for accurate temporal reasoning imposes a severe computational burden, primarily driven by the quadratic complexity of self-attention mechanisms in Large Language Models (LLMs). To alleviate this bottleneck, we propose ETA-VLA, an Efficient Token Adaptation framework for VLA models. ETA-VLA processes the past $n$ frames of multi-view images and introduces a novel Intra-LLM Sparse Aggregator (ILSA). Drawing inspiration from human driver attention allocation, ILSA dynamically identifies and prunes redundant visual tokens guided by textual queries and te...

• 2.Massive Parallel Deep Reinforcement Learning for Active SLAM

  arXiv:2603.25834v1 Announce Type: new Abstract: Recent advances in parallel computing and GPU acceleration have created new opportunities for computation-intensive learning problems such as Active SLAM -- where actions are selected to reduce uncertainty and improve joint mapping and localization. However, existing DRL-based approaches remain constrained by the lack of scalable parallel training. In this work, we address this challenge by proposing a scalable end-to-end DRL framework for Active SLAM that enables massively parallel training. Compared with the state of the art, our method significantly reduces training time, supports continuous action spaces and facilitates the exploration of more realistic scenarios. It is released as an open-source framework to promote reproducibility and community adoption.

• 3.Chasing Autonomy: Dynamic Retargeting and Control Guided RL for Performant and Controllable Humanoid Running

  arXiv:2603.25902v1 Announce Type: new Abstract: Humanoid robots have the promise of locomoting like humans, including fast and dynamic running. Recently, reinforcement learning (RL) controllers that can mimic human motions have become popular as they can generate very dynamic behaviors, but they are often restricted to single motion play-back which hinders their deployment in long duration and autonomous locomotion. In this paper, we present a pipeline to dynamically retarget human motions through an optimization routine with hard constraints to generate improved periodic reference libraries from a single human demonstration. We then study the effect of both the reference motion and the reward structure on the reference and commanded velocity tracking, concluding that a goal-conditioned and control-guided reward which tracks dynamically o...

• 4.Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories

  arXiv:2603.25903v1 Announce Type: new Abstract: Scaling robot learning to long-horizon tasks remains a formidable challenge. While end-to-end policies often lack the structural priors needed for effective long-term reasoning, traditional neuro-symbolic methods rely heavily on hand-crafted symbolic priors. To address the issue, we introduce ENAP (Emergent Neural Automaton Policy), a framework that allows a bi-level neuro-symbolic policy adaptively emerge from visuomotor demonstrations. Specifically, we first employ adaptive clustering and an extension of the L* algorithm to infer a Mealy state machine from visuomotor data, which serves as an interpretable high-level planner capturing latent task modes. Then, this discrete structure guides a low-level reactive residual network to learn precise continuous control via behavior cloning (BC). B...

• 5.Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned

  arXiv:2603.25937v1 Announce Type: new Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations. Despite growing real-world deployment, existing evaluations rely almost exclusively on success rate, whether the robot reaches its goal, which conceals trajectory quality, collision behavior, and robustness to environmental change. We present a real-world evaluation of five state-of-the-art VNMs (GNM, ViNT, NoMaD, NaviBridger, and CrossFormer) across two robot platforms and five environments spanning indoor and outdoor settings. Beyond success rate, we combine path-based metrics with vision-based goal-recognition scores and assess robustness through controlled image perturbations (motion blur, sunflare). Our analysis uncovers three systematic limitations: (a) even archi...

• 1.From Rich Text to Video: RCS Universal Profile 4.0 has arrived

  Summary available at source link.

• 2.Mobile Money accounted for $2 trillion in  transactions in 2025, doubling since 2021 as active accounts continue to grow

  Summary available at source link.

• 3.Strengthening the Global Fight Against Fraud and Scams – Takeaways from the Global Fraud Summit in Vienna

  Summary available at source link.

• 4.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

• 5.From Ambition to Execution: How Open Gateway Is Scaling the Global API Economy

  Summary available at source link.

• 1.Meta-Learned Adaptive Optimization for Robust Human Mesh Recovery with Uncertainty-Aware Parameter Updates

  Human mesh recovery from single images remains challenging due to inherent depth ambiguity and limited generalization across domains. While recent methods combine regression and optimization approaches, they struggle with poor initialization for test-time refinement and inefficient parameter updates during optimization. We propose a novel meta-learning framework that trains models to produce optimization-friendly initializations while incorporating uncertainty-aware adaptive updates during test-time refinement. Our approach introduces three key innovations: (1) a meta-learning strategy that simulates test-time optimization during training to learn better parameter initializations, (2) a selective parameter caching mechanism that identifies and freezes converged joints to reduce computational overhead, and (3) distribution-based adaptive u...

• 2.Interpretable long-term traffic modelling on national road networks using theory-informed deep learning

  Long-term traffic modelling is fundamental to transport planning, but existing approaches often trade off interpretability, transferability, and predictive accuracy. Classical travel demand models provide behavioural structure but rely on strong assumptions and extensive calibration, whereas generic deep learning models capture complex patterns but often lack theoretical grounding and spatial transferability, limiting their usefulness for long-term planning applications. We propose DeepDemand, a theory-informed deep learning framework that embeds key components of travel demand theory to predict long-term highway traffic volumes using external socioeconomic features and road-network structure. The framework integrates a competitive two-source Dijkstra procedure for local origin-destination (OD) region extraction and OD pair screening with...

• 3.On associative neural networks for sparse patterns with huge capacities

  Generalized Hopfield models with higher-order or exponential interaction terms are known to have substantially larger storage capacities than the classical quadratic model. On the other hand, associative memories for sparse patterns, such as the Willshaw and Amari models, already outperform the classical Hopfield model in the sparse regime. In this paper we combine these two mechanisms. We introduce higher-order versions of sparse associative memory models and study their storage capacities. For fixed interaction order $n$, we obtain storage capacities of polynomial order in the system size. When the interaction order is allowed to grow logarithmically with the number of neurons, this yields super-polynomial capacities. We also discuss an analogue in the Gripon--Berrou architecture which was formulated for non-sparse messages (see \cite{g...

• 4.A Human-Inspired Decoupled Architecture for Efficient Audio Representation Learning

  While self-supervised learning (SSL) has revolutionized audio representation, the excessive parameterization and quadratic computational cost of standard Transformers limit their deployment on resource-constrained devices. To address this bottleneck, we propose HEAR (Human-inspired Efficient Audio Representation), a novel decoupled architecture. Inspired by the human cognitive ability to isolate local acoustic features from global context, HEAR splits the processing pipeline into two dedicated modules: an Acoustic Model for local feature extraction and a Task Model for global semantic integration. Coupled with an Acoustic Tokenizer trained via knowledge distillation, our approach enables robust Masked Audio Modeling (MAM). Extensive experiments demonstrate that HEAR requires only 15M parameters and 9.47 GFLOPs for inference, operating at ...

• 5.EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset

  The progress of Anomaly Detection (AD) in safety-critical domains, such as transportation, is severely constrained by the lack of large-scale, real-world benchmarks. To address this, we introduce EngineAD, a novel, multivariate dataset comprising high-resolution sensor telemetry collected from a fleet of 25 commercial vehicles over a six-month period. Unlike synthetic datasets, EngineAD features authentic operational data labeled with expert annotations, distinguishing normal states from subtle indicators of incipient engine faults. We preprocess the data into $300$-timestep segments of $8$ principal components and establish an initial benchmark using nine diverse one-class anomaly detection models. Our experiments reveal significant performance variability across the vehicle fleet, underscoring the challenge of cross-vehicle generalizati...

• 1.Why Are Large Language Models so Terrible at Video Games?

  Large language models (LLMs) have improved so quickly that the benchmarks themselves have evolved, adding more complex problems in an effort to challenge the latest models. Yet LLMs haven’t improved across all domains, and one task remains far outside their grasp: They have no idea how to play video games. While a few have managed to beat a few games (for example, Gemini 2.5 Pro beat Pokemon Blue in May of 2025), these exceptions prove the rule. The eventually victorious AI completed games far more slowly than a typical human player, made bizarre and often repetitive mistakes, and required custom software to guide their interactions with the game. Julian Togelius , the director of New York University’s Game Innovation Lab and co-founder of AI game testing company Modl.ai, explored the implications of LLMs’ limitations in video games in a ...

• 2.How NYU’s Quantum Institute Bridges Science and Application

  This sponsored article is brought to you by NYU Tandon School of Engineering . Within a 6 mile radius of New York University’s (NYU) campus, there are more than 500 tech industry giants, banks, and hospitals. This isn’t just a fact about real estate, it’s the foundation for advancing quantum discovery and application. While the world races to harness quantum technology, NYU is betting that the ultimate advantage lies not solely in a lab, but in the dense, demanding, and hyper-connected urban ecosystem that surrounds it. With the launch of its NYU Quantum Institute (NYUQI), NYU is positioning itself as the central node in this network; a “full stack” powerhouse built on the conviction that it has found the right place, and the right time, to turn quantum science into tangible reality. Proximity advantage is essential because quantum scienc...

• 3.Training Driving AI at 50,000× Real Time

  This is a sponsored article brought to you by General Motors. Visit their new Engineering Blog for more insights. Autonomous driving is one of the most demanding problems in physical AI. An automated system must interpret a chaotic, ever-changing world in real time—navigating uncertainty, predicting human behavior, and operating safely across an immense range of environments and edge cases. At General Motors, we approach this problem from a simple premise: while most moments on the road are predictable, the rare, ambiguous, and unexpected events — the long tail — are what ultimately defines whether an autonomous system is safe, reliable, and ready for deployment at scale. (Note: While here we discuss research and emerging technologies to solve the long tail required for full general autonomy, we also discuss our current approach or solvin...

• 4.What Happens When You Host an AI Café

  “Can I get an interview?” “Can I get a job when I graduate?” Those questions came from students during a candid discussion about artificial intelligence, capturing the anxiety many young people feel today. As companies adopt AI-driven interview screeners, restructure their workforces, and redirect billions of dollars toward AI infrastructure , students are increasingly unsure of what the future of work will look like. We had gathered people together at a coffee shop in Auburn, Alabama, for what we called an AI Café. The event was designed to confront concerns about AI directly, demystifying the technology while pushing back against the growing narrative of technological doom. AI is reshaping society at breathtaking speed. Yet the trajectory of this transformation is being charted primarily by for-profit tech companies, whose priorities re...

• 5.These AI Workstations Look Like PCs but Pack a Stronger Punch

  The rise of generative AI has spurred demand for AI workstations that can run or train models on local hardware. Yet modern PCs have proven inadequate for this task . A typical laptop has only enough memory to load a large language model (LLM) with 8 billion to 13 billion parameters—much smaller, and much less intelligent, than frontier models that are presumed to have over a trillion parameters. Even the most capable workstation PCs struggle to serve LLMs with more than 70 billion parameters. Tenstorrent’s QuietBox 2 is an attempt to fill that gap. Though it looks like a PC workstation, the QuietBox 2 contains four of the company’s custom Blackhole AI accelerators, 128 gigabytes of GDDR6 memory—specialized memory used in GPUs—and 256 GB of DDR5 system memory (for a total of 384 GB). This configuration provides enough memory to load OpenA...

• 1.When Not to Use AI

  Carolyn Geason-Beissel/MIT SMR | Getty Images AI promises to make managers more productive and give them access to more information more quickly. It can draft plans, summarize reports, and even coach you on how to deliver feedback. Yet the same technology that accelerates decision-making can also erode your judgment, if you let it. Rely on […]

• 2.How Morningstar’s CEO Drives Relentless Execution

  Aleksandar Savic Many investors rely on Morningstar for independent financial analysis and insights, but few people are familiar with the company behind the ratings. From Morningstar’s origins rating mutual funds, the company has expanded its product line, customer base, and global footprint and realized a tenfold increase in revenues and profits between 2005 and 2025. […]

• 3.An AI Reckoning for HR: Transform or Fade Away

  Carolyn Geason-Beissel/MIT SMR | Getty Images For decades, human resource leaders have talked about the need to shift their focus from having responsibility for compliance to acting as architects of talent strategy. And for decades, the pattern of HR being stuck in age-old roles has persisted. But there is new pressure to redefine the role. […]

• 4.Shifting AI From Fear to Optimism: U.S. Department of Labor’s Taylor Stockton

  In this episode of the Me, Myself, and AI podcast, host Sam Ransbotham speaks with Taylor Stockton, chief innovation officer at the U.S. Department of Labor, about how artificial intelligence is reshaping the workforce. Taylor emphasizes that AI is having an economywide impact, transforming tasks within nearly every job rather than affecting only certain industries […]

• 5.Why Leaders Lose the Room in High-Stakes Meetings

  Carolyn Geason-Beissel/MIT SMR | Getty Images Most advice about leadership communication focuses on presentation skills: Be concise, be clear, tell better stories. But the most consequential leadership communication happens in meetings where tough issues are being discussed and real decisions are being made. Even some of the most skilled leaders find themselves in moments where […]

• 1.Preferences for Warning Signal Quality: Experimental Evidence -- by Alexander Ugarov, Arya Gaduh, Peter McGee

  We use a laboratory experiment to study preferences over false-positive and false-negative rates of warning signals for an adverse event with a known prior. We find that subjects decrease their demand with signal quality, but less than predicted by our theory. There is asymmetric under-responsiveness by prior: for a low (high) prior, their willingness-to-pay does not fully adjust for the increase in the false-positive (false-negative) costs. We show that neither risk preference nor Bayesian updating skills can fully explain our results. Our results are most consistent with a decision-making heuristic in which subjects do not distinguish between false-positive and false-negative errors.

• 2.Bank Fees and Household Financial Well-Being -- by Michaela Pagel, Sharada Sridhar, Emily Williams

  In this study, we examine policy changes from large U.S. banks between 2017 and 2022, which eliminated non-sufficient funds (NSF) fees and relaxed overdraft policies. Using individual transaction-level data, we find that the elimination of NSF fees, not surprisingly, resulted in immediate reductions in NSF charges across the income distribution. However, relaxing overdraft policies resulted in reductions in overdraft fees only for wealthier households, along the dimensions of income and liquidity, and only those enjoyed subsequent declines in late fees, interest payments, account maintenance fees, and the use of alternative financial services, such as payday loans. Our results thus suggest that the policy changes were not substantial enough to significantly reduce the financial stress of the more vulnerable households. As our setting feat...

• 3.Steering Technological Progress -- by Anton Korinek, Joseph E. Stiglitz

  Rapid progress in new technologies such as AI has led to widespread anxiety about adverse labor market impacts. This paper asks how to guide innovative efforts so as to increase labor demand and create better-paying jobs while also evaluating the limitations of such an approach. We develop a theoretical framework to identify the properties that make an innovation desirable from the perspective of workers, including its technological complementarity to labor, the relative income of the affected workers, and the factor share of labor in producing the goods involved. Applications include robot taxation, factor-augmenting progress, and task automation. In our framework, the welfare benefits of steering technology are greater the less efficient social safety nets are. As technological progress devalues labor, the welfare benefits of steering a...

• 4.Mind the Gap: AI Adoption in Europe and the U.S. -- by Alexander Bick, Adam Blandin, David J. Deming, Nicola Fuchs-Schündeln, Jonas Jessen

  This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence t...

• 5.Supporting Student Engagement During Remote Learning: Three Randomized Controlled Trials in Chicago Public Schools -- by Monica P. Bhatt, Jonathan Guryan, Fatemeh Momeni, Philip Oreopoulos, Eleni Packis

  This paper presents the results of three field experiments testing interventions designed to increase engagement and improve learning during remote schooling. Since the COVID-19 pandemic, the use of remote learning when schooling is interrupted has become more common, prompting educators to ask: How can we better engage students during remote instruction? This is especially salient because much of what we know about student engagement is based on in-person schooling, not virtual instruction. In the first experiment, we find that personalized phone calls increased families’ likelihood of registering for a virtual summer schooling program in Chicago Public Schools, the pre-specified primary outcome. In the second experiment, we find sending weekly text messages had no effect on students’ summer days absent and usage of Khan Academy, the pri...

• 1.Sports Betting Is Everywhere, Especially on Credit Reports

  Since 2018, more than thirty states have legalized mobile sports betting, leading to more than a half trillion dollars in wagers. In our recent Staff Report, we examine how legalized sports betting affects household financial health by comparing betting activity and consumer credit outcomes between states that legalized to those that have not. We find that legalization increases spending at online sportsbooks roughly tenfold, but betting does not stop at state boundaries. Nearby areas where betting is not legal still experience roughly 15 percent the increase of counties where it is legal. At the same time, consumer financial health suffers. Our analysis finds rising delinquencies in participating states,...

• 2.China’s Electric Trade

  China has spent considerable government resources to develop advanced electric technology industries, such as those that produce electric vehicles, lithium batteries, and solar panels. These efforts have spilled over to international trade as improvements in price and quality have increased the global demand for these goods. One consequence is that passenger cars and batteries have been disproportionately large contributors to the rise in the country’s trade surplus in recent years. This has not been the case, though, for solar panels, as falling prices due to a supply glut pulled down export revenues despite higher volumes.

• 3.The New York Fed DSGE Model Forecast—March 2026

  This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since December 2025. To summarize, growth in 2026 is expected to be more robust, and inflation more persistent, than predicted in December. Stronger investment is the main driver for higher growth, while cost-push shocks, possibly capturing the effects of tariffs, are the key factors behind higher inflation. Projections for the short-run real natural rate of interest (r*) are the same as in December.

• 4.Firms’ Inflation Expectations Return to 2024 Levels

  Businesses experienced substantial cost pressures in 2025 as the cost of insurance and utilities rose sharply, while an increase in tariffs contributed to rising goods and materials costs. This post examines how firms in the New York-Northern New Jersey region adjusted their prices in response to these cost pressures and describes their expectations for future price increases and inflation. Survey results show an acceleration in firms’ price increases in 2025, with an especially sharp increase in the manufacturing sector. While both cost and price increases intensified last year, our surveys re...

• 5.Are Rising Employee Health Insurance Costs Dampening Wage Growth?

  Employer-sponsored health insurance represents a substantial component of total compensation paid by firms to many workers in the United States. Such costs have climbed by close to 20 percent over the past five years. Indeed, the average annual premium for employer-sponsored family health insurance coverage was about $27,000 in 2025—roughly equivalent to the wage of a full-time worker paid $15 per hour. Our February regional business surveys asked firms whether their wage setting decisions were influenced by the rising cost of employee health insurance. As we showed in our

  Project Syndicate

RCR Wireless

Semantic Scholar – Machine Learning

arXiv Quantitative Finance

arXiv – 6G & Networking

arXiv – Network Architecture (6G/Slicing)